[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

On March 11 th 2020, World Health Organization (WHO) declared the 2019 novel corona virus as global pandemic. Corona virus, also known as COVID-19 was first originated in Wuhan, Hubei province in China around December 2019 and spread out all over the world within few weeks. Based on the public datasets provided by John Hopkins university and Canadian health authority, we have developed a forecasting model of COVID-19 outbreak in Canada using state-of-the-art Deep Learning (DL) models. In this novel research, we evaluated the key features to predict the trends and possible stopping time of the current COVID-19 outbreak in Canada and around the world. In this paper we presented the Long short-term memory (LSTM) networks, a deep learning approach to forecast the future COVID-19 cases. Based on the results of our Long short-term memory (LSTM) network, we predicted the possible ending point of this outbreak will be around June 2020. In addition to that, we compared transmission rates of Canada with Italy and USA. Here we also presented the 2, 4, 6, 8, 10, 12 and 14 th day predictions for 2 successive days. Our forecasts in this paper is based on the available data until March 31, 2020. To the best of our knowledge, this of the few studies to use LSTM networks to forecast the infectious diseases.

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Time Series Forecasting of COVID-19 transmission in Canada Using LSTM Networks.

Deterministic and Stochastic Optimal Control

COVID-19, responsible of infecting billions of people and economy across the globe, requires detailed study of the trend it follows to develop adequate short-term prediction models for forecasting the number of future cases. In this perspective, it is possible to develop strategic planning in the public health system to avoid deaths as well as managing patients. In this paper, proposed forecast models comprising autoregressive integrated moving average (ARIMA), support vector regression (SVR), long shot term memory (LSTM), bidirectional long short term memory (Bi-LSTM) are assessed for time series prediction of confirmed cases, deaths and recoveries in ten major countries affected due to COVID-19. The performance of models is measured by mean absolute error, root mean square error and r2_score indices. In the majority of cases, Bi-LSTM model outperforms in terms of endorsed indices. Models ranking from good performance to the lowest in entire scenarios is Bi-LSTM, LSTM, GRU, SVR and ARIMA. Bi-LSTM generates lowest MAE and RMSE values of 0.0070 and 0.0077, respectively, for deaths in China. The best r2_score value is 0.9997 for recovered cases in China. On the basis of demonstrated robustness and enhanced prediction accuracy, Bi-LSTM can be exploited for pandemic prediction for better planning and management.

Predictions for COVID-19 with Deep Learning Models of LSTM, GRU and Bi-LSTM

This paper addresses the synergistic interaction between HIV and
mycobacterium tuberculosis using a deterministic model, which incorporates
many of the essential biological and epidemiological features of the two dis-
eases. In the absence of TB infection, the model (HIV-only model) is shown
to have a globally asymptotically stable, disease-free equilibrium whenever the
associated reproduction number is less than unity and has a unique endemic
equilibrium whenever this number exceeds unity. On the other hand, the
model with TB alone (TB-only model) undergoes the phenomenon of back-
ward bifurcation, where the stable disease-free equilibrium co-exists with a
stable endemic equilibrium when the associated reproduction threshold is less
than unity. The analysis of the respective reproduction thresholds shows that
the use of a targeted HIV treatment (using anti-retroviral drugs) strategy can
lead to effective control of HIV provided it reduces the relative infectiousness
of individuals treated (in comparison to untreated HIV-infected individuals)
below a certain threshold. The full model, with both HIV and TB, is simu-
lated to evaluate the impact of the various treatment strategies. It is shown
that the HIV-only treatment strategy saves more cases of the mixed infection
than the TB-only strategy. Further, for low treatment rates, the mixed-only
strategy saves the least number of cases (of HIV, TB, and the mixed infection)
in comparison to the other strategies. Thus, this study shows that if resources
are limited, then targeting such resources to treating one of the diseases is
more beneficial in reducing new cases of the mixed infection than targeting
the mixed infection only diseases. Finally, the universal strategy saves more
cases of the mixed infection than any of the other strategies.

Mathematical analysis of the transmission dynamics of HIV/TB coinfection in the presence of treatment

The phenomenon of backward bifurcation in disease models, where a stable endemic equilibrium coexists with a stable disease-free equilibrium when the associated reproduction number is less than unity, has important implications for disease control. In such a scenario, the classical requirement of the reproduction number being less than unity becomes only a necessary, but not sufficient, condition for disease elimination. This paper addresses the role of the choice of incidence function in a vaccine-induced backward bifurcation in HIV models. Several examples are given where backward bifurcations occur using standard incidence, but not with their equivalents that employ mass action incidence. Furthermore, this result is independent of the type of vaccination program adopted. These results emphasize the need for further work on the incidence functions used in HIV models.

Role of incidence function in vaccine-induced backward bifurcation in some HIV models.

Curtailing smoking dynamics: a mathematical modeling approach

Mathematical modelling of infectious diseases has shown that combinations of isolation, quarantine, vaccine and treatment are often necessary in order to eliminate most infectious diseases. However, if they are not administered at the right time and in the right amount, the disease elimination will remain a difficult task. Optimal control theory has proven to be a successful tool in understanding ways to curtail the spread of infectious diseases by devising the optimal diseases intervention strategies. The method consists of minimizing the cost of infection or the cost of implementing the control, or both. This paper reviews the available literature on mathematical models that use optimal control theory to deduce the optimal strategies aimed at curtailing the spread of an infectious disease.

Oluwaseun Sharomi

Papers

Mathematical analysis of the transmission dynamics of HIV/TB coinfection in the presence of treatment

Role of incidence function in vaccine-induced backward bifurcation in some HIV models.

Curtailing smoking dynamics: a mathematical modeling approach

Optimal control in epidemiology

Re-infection-induced backward bifurcation in the transmission dynamics of Chlamydia trachomatis